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Unable to Attach Databricks Serverless Compute – Environment Version 5 (Heavy Load Warning)

NaniOnyx07
New Contributor

Hi Databricks Community,

I'm experiencing an issue with Databricks Serverless Compute while trying to attach it to my notebook using Serverless Environment Version 5.

Whenever I attempt to attach the compute, I receive the following warning: "Databricks is experiencing heavy load. Operations may take longer to complete than usual."

Issue Details:

  • Compute Type: Serverless

  • Environment Version: 5

  • Issue: Unable to attach Serverless Compute to notebooks

  • Duration: Since yesterday evening (November 5, 2026)

  • Current Status: Issue is still ongoing

I've tried attaching the Serverless Compute multiple times, but the same warning continues to appear, and the attachment does not complete.

Questions:

  1. Is anyone else currently experiencing this issue with Serverless Compute?

  2. Is this related to a temporary Databricks infrastructure issue or serverless capacity limitations?

  3. Are there any recommended troubleshooting steps or workarounds?

  4. Is there a known issue specifically affecting Serverless Environment Version 5?

I would appreciate any guidance from the community or Databricks team on how to resolve this.

Thanks in advance! Screenshot 2026-11-06 085609.png

 

2 REPLIES 2

ThomazNeto
Databricks Partner

Hi,

I checked status.databricks.com today and there's no open or recent incident. Notebooks and Compute show as operational in all regions. There's also no known issues section on the environment version 5 release notes. Version 5 was released in February 2026 and is supported until February 2029, so it's not being retired.

A few things that help isolate it:

1. Try a different environment version. Per the docs, in serverless notebooks the environment version is part of the base environment selection in the Environment side panel. Switch to another one (version 4, or version 6, the newest) and attach again. If that works, the problem is specific to your version 5 setup and not serverless in general.

2. Try a brand new, empty notebook. If it attaches there, compare the Environment panel of the two notebooks, including any dependencies you added.

3. If this is a Free Edition workspace, check your daily quota. The Free Edition docs say that if you exceed it, compute is shut down and unavailable for the rest of the day, while your data and settings are kept.

If none of that helps and you're on a paid workspace, open a support case with the workspace ID and the notebook URL. Free Edition isn't covered by the support policy, so in that case this thread is the right place.

Docs:
Serverless environment versions
Environment version 5
Free Edition limitations
Status page

 

 

Thomaz A. Rossito Neto
Principal Data Architect & AI Strategy — CI&T
thomazn@ciandt.com
linkedin.com/in/thomaz-antonio-rossito-neto | Databricks Solutions Architect Champion

Louis_Frolio
Databricks Employee
Databricks Employee

Hello @NaniOnyx07, I took a look at both internal and external documentation and here is what I found.

First, credit to @ThomazNeto for the thorough first pass. I checked his facts against the docs and he is spot on: environment version 5 shipped in February 2026, is supported through February 2029, and has no known-issues entry. His steps (try version 4 or 6, try a brand new empty notebook, check your Free Edition quota) are where I'd start too, so I'll build on them rather than repeat them.

One thing to understand about the banner. "Databricks is experiencing heavy load" is a generic workspace-level message, not a serverless error and not a version 5 error. In my experience it shows up when calls from your browser to the control plane are slow or getting throttled. It's telling you the workspace UI is struggling, and the environment version is just where you happened to be when it did. So there's likely nothing to fix in your version 5 setup.

Two questions that help:

Which cloud and region? The page Thomaz checked (status.databricks.com) covers AWS. Azure has its own at status.azuredatabricks.net, and GCP has a separate page as well. "Since yesterday evening" is a long time for a capacity blip, so if there's a regional issue on your cloud, it'll be there.

Is anyone else in the same workspace seeing it? If a colleague can attach fine, it's your session or your notebook. If nobody can, it's the workspace or the region.

Then a short sequence:

  1. Reset the browser side. Sign out, hard refresh, and try an incognito window or a different browser with extensions off. That banner can get sticky in a stale session.
  2. Check whether serverless itself is healthy in your workspace. Run a query on a serverless SQL warehouse, or create a one-cell job on serverless compute. If those work, capacity is fine and the problem is in the notebook attach path.
  3. Look at the notebook's environment. Open the Environment panel on the right, switch the version, click Apply, and attach again. If the notebook was imported or came in through Git as a source-format file, pay extra attention here: source format only carries code, so the environment settings may not have come with it. Credit to @Thomas_W and @LonaOsmani in an older thread (linked below) for spotting that imported notebooks with a stale pinned version wouldn't attach until they switched it. Also check the Dependencies list. Installing PySpark, or any library that pulls PySpark in, stops the session per the docs. If you see anything like that, remove it, then click the arrow next to Apply and choose Reset to defaults to clear the cached environment.

If you're on Free Edition, keep in mind that exceeding the quota shuts compute down for the rest of the day (data and settings are kept, and it comes back when the limit resets), and Free Edition isn't covered by support, so this thread is the right place. If you're on a paid workspace and it's still happening after the browser reset and a colleague sees it too, open a support case with the workspace ID, cloud and region, UTC timestamps, environment version, notebook URL, a screenshot, and any request or error ID shown. Keep those identifiers out of the public thread.

References:

Regards, Louis.